MurSS: Multi-resolution Selective Segmentation Model for Breast Cancer

Abstract

We propose the Multi-resolution Selective Segmentation model (MurSS) for segmenting benign, Ductal Carcinoma In Situ, and Invasive Ductal Carcinoma in breast resection Hematoxylin and Eosin stained Whole Slide Images. MurSS simultaneously trains on context information from a wide area at low resolution and content information from a local area at high resolution, aiming for a more accurate diagnosis. Additionally, through the selection stage, it provides solutions for ambiguous tissue regions. Our proposed MurSS achieves a mean Intersection of Union performance of 91.1%, which is at least 16.8% and at most 19.0% higher than well-known image segmentation models.